Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric9
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has unique valuesUnique
nox((g/km)) has unique valuesUnique
hc((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:40:30.998805
Analysis finished2023-12-10 13:40:43.542069
Duration12.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:43.632757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:40:43.798598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
건기연
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건기연
2nd row건기연
3rd row건기연
4th row건기연
5th row건기연

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T22:40:43.974707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:44.071141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:40:44.290088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0134-0]
2nd row[0134-0]
3rd row[0141-1]
4th row[0141-1]
5th row[0142-0]
ValueCountFrequency (%)
0134-0 2
 
2.0%
4302-3 2
 
2.0%
4606-2 2
 
2.0%
3804-2 2
 
2.0%
3906-1 2
 
2.0%
3906-4 2
 
2.0%
3907-1 2
 
2.0%
3918-2 2
 
2.0%
4205-1 2
 
2.0%
4206-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:40:44.749653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
8 24
 
3.0%
Other values (3) 60
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 122
24.4%
3 82
16.4%
1 66
13.2%
4 64
12.8%
2 54
10.8%
7 28
 
5.6%
8 24
 
4.8%
6 22
 
4.4%
5 22
 
4.4%
9 16
 
3.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
8 24
 
3.0%
Other values (3) 60
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
8 24
 
3.0%
Other values (3) 60
7.5%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T22:40:44.915118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:45.019841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:40:45.236003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.22
Min length4

Characters and Unicode

Total characters522
Distinct characters81
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송탄-오산
2nd row송탄-오산
3rd row고양-파주
4th row고양-파주
5th row당동-파평
ValueCountFrequency (%)
송탄-오산 2
 
2.0%
봉담-향남 2
 
2.0%
청평-가평 2
 
2.0%
안성-죽산 2
 
2.0%
아산만-덕목 2
 
2.0%
발안ic-청북ic 2
 
2.0%
팔탄-비봉 2
 
2.0%
일영-의정부 2
 
2.0%
보라-용인 2
 
2.0%
용인-마장 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:40:45.677007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.2%
평 28
 
5.4%
천 24
 
4.6%
부 16
 
3.1%
주 14
 
2.7%
양 14
 
2.7%
안 14
 
2.7%
성 12
 
2.3%
중 12
 
2.3%
동 10
 
1.9%
Other values (71) 278
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 414
79.3%
Dash Punctuation 100
 
19.2%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
평 28
 
6.8%
천 24
 
5.8%
부 16
 
3.9%
주 14
 
3.4%
양 14
 
3.4%
안 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
청 10
 
2.4%
Other values (68) 260
62.8%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 414
79.3%
Common 100
 
19.2%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
평 28
 
6.8%
천 24
 
5.8%
부 16
 
3.9%
주 14
 
3.4%
양 14
 
3.4%
안 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
청 10
 
2.4%
Other values (68) 260
62.8%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 414
79.3%
ASCII 108
 
20.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
평 28
 
6.8%
천 24
 
5.8%
부 16
 
3.9%
주 14
 
3.4%
양 14
 
3.4%
안 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
청 10
 
2.4%
Other values (68) 260
62.8%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.394
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:45.867562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.05
Q310.6
95-th percentile17.6
Maximum27.5
Range26
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation4.9335139
Coefficient of variation (CV)0.5877429
Kurtosis3.1436001
Mean8.394
Median Absolute Deviation (MAD)2.65
Skewness1.4663356
Sum839.4
Variance24.33956
MonotonicityNot monotonic
2023-12-10T22:40:46.062269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6.0 4
 
4.0%
4.8 4
 
4.0%
5.4 4
 
4.0%
10.6 4
 
4.0%
6.1 4
 
4.0%
6.2 2
 
2.0%
5.6 2
 
2.0%
2.7 2
 
2.0%
27.5 2
 
2.0%
7.2 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.6 2
2.0%
2.7 2
2.0%
2.9 2
2.0%
3.1 2
2.0%
3.4 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
4.8 4
4.0%
ValueCountFrequency (%)
27.5 2
2.0%
19.0 2
2.0%
17.6 2
2.0%
15.5 2
2.0%
15.4 2
2.0%
14.7 2
2.0%
14.6 2
2.0%
12.9 2
2.0%
12.6 2
2.0%
12.1 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T22:40:46.212650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:46.653110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 100
100.0%

측정시간
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T22:40:46.764288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:46.882518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.435444
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:47.028565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.380095
Q337.71834
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.55332

Descriptive statistics

Standard deviation0.33487706
Coefficient of variation (CV)0.0089454544
Kurtosis-1.0532316
Mean37.435444
Median Absolute Deviation (MAD)0.266565
Skewness0.34387861
Sum3743.5444
Variance0.11214264
MonotonicityNot monotonic
2023-12-10T22:40:47.217924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.43583 2
 
2.0%
36.95866 2
 
2.0%
37.05786 2
 
2.0%
37.23589 2
 
2.0%
37.71834 2
 
2.0%
37.23653 2
 
2.0%
37.2375 2
 
2.0%
37.29776 2
 
2.0%
37.14026 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.95627 2
2.0%
36.95866 2
2.0%
36.96033 2
2.0%
36.98521 2
2.0%
37.00613 2
2.0%
37.01644 2
2.0%
37.02727 2
2.0%
37.05786 2
2.0%
37.08149 2
2.0%
37.09529 2
2.0%
ValueCountFrequency (%)
38.06264 2
2.0%
38.06053 2
2.0%
38.0169 2
2.0%
37.99285 2
2.0%
37.96019 2
2.0%
37.94255 2
2.0%
37.91419 2
2.0%
37.87708 2
2.0%
37.83225 2
2.0%
37.76396 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.22314
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:47.401356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.06364
median127.2374
Q3127.37997
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.31633

Descriptive statistics

Standard deviation0.24306995
Coefficient of variation (CV)0.0019105796
Kurtosis-0.73130917
Mean127.22314
Median Absolute Deviation (MAD)0.172565
Skewness0.080976282
Sum12722.314
Variance0.059083001
MonotonicityNot monotonic
2023-12-10T22:40:47.620258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
127.26262 2
 
2.0%
126.92256 2
 
2.0%
126.92508 2
 
2.0%
126.88236 2
 
2.0%
126.92818 2
 
2.0%
127.166 2
 
2.0%
127.3107 2
 
2.0%
127.60231 2
 
2.0%
126.91531 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.77946 2
2.0%
126.82904 2
2.0%
126.83253 2
2.0%
126.85908 2
2.0%
126.88236 2
2.0%
126.88641 2
2.0%
126.91531 2
2.0%
126.92256 2
2.0%
126.92508 2
2.0%
126.92818 2
2.0%
ValueCountFrequency (%)
127.74421 2
2.0%
127.6367 2
2.0%
127.62684 2
2.0%
127.61201 2
2.0%
127.60231 2
2.0%
127.56566 2
2.0%
127.55994 2
2.0%
127.49045 2
2.0%
127.44335 2
2.0%
127.44132 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.6714
Minimum3.93
Maximum919.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:47.793125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.93
5-th percentile13.042
Q153.675
median112.51
Q3196.685
95-th percentile372.6785
Maximum919.03
Range915.1
Interquartile range (IQR)143.01

Descriptive statistics

Standard deviation160.69983
Coefficient of variation (CV)1.0257126
Kurtosis9.8630481
Mean156.6714
Median Absolute Deviation (MAD)67.33
Skewness2.7399201
Sum15667.14
Variance25824.435
MonotonicityNot monotonic
2023-12-10T22:40:47.949133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
288.85 1
 
1.0%
285.83 1
 
1.0%
300.01 1
 
1.0%
187.33 1
 
1.0%
183.17 1
 
1.0%
75.59 1
 
1.0%
56.74 1
 
1.0%
169.68 1
 
1.0%
135.15 1
 
1.0%
163.0 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
3.93 1
1.0%
4.93 1
1.0%
8.47 1
1.0%
11.85 1
1.0%
11.94 1
1.0%
13.1 1
1.0%
17.05 1
1.0%
17.99 1
1.0%
22.8 1
1.0%
24.58 1
1.0%
ValueCountFrequency (%)
919.03 1
1.0%
898.1 1
1.0%
764.57 1
1.0%
447.78 1
1.0%
431.93 1
1.0%
369.56 1
1.0%
343.3 1
1.0%
317.69 1
1.0%
307.15 1
1.0%
303.2 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.3486
Minimum2.28
Maximum889.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:48.128870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.28
5-th percentile9.177
Q142.62
median104.755
Q3186.4
95-th percentile320.333
Maximum889.52
Range887.24
Interquartile range (IQR)143.78

Descriptive statistics

Standard deviation147.4731
Coefficient of variation (CV)1.0895798
Kurtosis11.228832
Mean135.3486
Median Absolute Deviation (MAD)63.735
Skewness2.9255839
Sum13534.86
Variance21748.316
MonotonicityNot monotonic
2023-12-10T22:40:48.283808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
223.41 1
 
1.0%
246.44 1
 
1.0%
208.63 1
 
1.0%
141.91 1
 
1.0%
162.02 1
 
1.0%
54.07 1
 
1.0%
50.21 1
 
1.0%
170.43 1
 
1.0%
136.64 1
 
1.0%
128.07 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2.28 1
1.0%
2.99 1
1.0%
4.53 1
1.0%
7.13 1
1.0%
7.79 1
1.0%
9.25 1
1.0%
11.72 1
1.0%
14.32 1
1.0%
14.53 1
1.0%
17.38 1
1.0%
ValueCountFrequency (%)
889.52 1
1.0%
790.46 1
1.0%
708.12 1
1.0%
383.85 1
1.0%
343.95 1
1.0%
319.09 1
1.0%
306.65 1
1.0%
290.16 1
1.0%
286.84 1
1.0%
276.86 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.6709
Minimum0.38
Maximum114.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:48.434233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.38
5-th percentile1.5685
Q16.32
median14.665
Q323.0725
95-th percentile45.4655
Maximum114.4
Range114.02
Interquartile range (IQR)16.7525

Descriptive statistics

Standard deviation19.929069
Coefficient of variation (CV)1.0673867
Kurtosis10.059898
Mean18.6709
Median Absolute Deviation (MAD)8.355
Skewness2.7851792
Sum1867.09
Variance397.16781
MonotonicityNot monotonic
2023-12-10T22:40:48.647959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.3 1
 
1.0%
36.01 1
 
1.0%
30.42 1
 
1.0%
21.47 1
 
1.0%
22.05 1
 
1.0%
7.58 1
 
1.0%
5.44 1
 
1.0%
22.51 1
 
1.0%
18.57 1
 
1.0%
20.04 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.38 1
1.0%
0.45 1
1.0%
0.78 1
1.0%
1.14 1
1.0%
1.16 1
1.0%
1.59 1
1.0%
1.75 1
1.0%
2.21 1
1.0%
2.28 1
1.0%
2.48 1
1.0%
ValueCountFrequency (%)
114.4 1
1.0%
108.7 1
1.0%
96.65 1
1.0%
57.24 1
1.0%
47.28 1
1.0%
45.37 1
1.0%
41.67 1
1.0%
41.43 1
1.0%
39.62 1
1.0%
38.43 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8621
Minimum0.13
Maximum57.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:48.883047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.4195
Q12.415
median5.805
Q310.2225
95-th percentile19.4015
Maximum57.53
Range57.4
Interquartile range (IQR)7.8075

Descriptive statistics

Standard deviation8.9598369
Coefficient of variation (CV)1.1396239
Kurtosis12.402653
Mean7.8621
Median Absolute Deviation (MAD)3.66
Skewness3.0330331
Sum786.21
Variance80.278677
MonotonicityNot monotonic
2023-12-10T22:40:49.089267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.84 2
 
2.0%
0.13 2
 
2.0%
6.13 2
 
2.0%
1.52 2
 
2.0%
3.51 2
 
2.0%
3.46 2
 
2.0%
10.55 1
 
1.0%
12.82 1
 
1.0%
9.82 1
 
1.0%
2.66 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.13 2
2.0%
0.14 1
1.0%
0.28 1
1.0%
0.41 1
1.0%
0.42 1
1.0%
0.56 1
1.0%
0.6 1
1.0%
0.68 1
1.0%
0.7 1
1.0%
0.84 2
2.0%
ValueCountFrequency (%)
57.53 1
1.0%
45.26 1
1.0%
39.78 1
1.0%
21.95 1
1.0%
21.71 1
1.0%
19.28 1
1.0%
19.14 1
1.0%
18.64 1
1.0%
17.87 1
1.0%
17.5 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38417.257
Minimum948.33
Maximum233142.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:49.299627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum948.33
5-th percentile3183.041
Q113678.865
median27245.085
Q351387.415
95-th percentile88543.346
Maximum233142.62
Range232194.29
Interquartile range (IQR)37708.55

Descriptive statistics

Standard deviation39457.07
Coefficient of variation (CV)1.0270663
Kurtosis10.580877
Mean38417.257
Median Absolute Deviation (MAD)15776.96
Skewness2.8419375
Sum3841725.7
Variance1.5568603 × 109
MonotonicityNot monotonic
2023-12-10T22:40:49.493495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67484.47 1
 
1.0%
66784.94 1
 
1.0%
77431.81 1
 
1.0%
43690.0 1
 
1.0%
45671.49 1
 
1.0%
17977.58 1
 
1.0%
15807.64 1
 
1.0%
39844.24 1
 
1.0%
32876.83 1
 
1.0%
37375.23 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
948.33 1
1.0%
1295.03 1
1.0%
2024.1 1
1.0%
2861.88 1
1.0%
3099.84 1
1.0%
3187.42 1
1.0%
4059.47 1
1.0%
4454.64 1
1.0%
5744.26 1
1.0%
6385.3 1
1.0%
ValueCountFrequency (%)
233142.62 1
1.0%
214432.0 1
1.0%
195118.72 1
1.0%
103485.33 1
1.0%
102960.85 1
1.0%
87784.53 1
1.0%
80403.64 1
1.0%
77431.81 1
1.0%
75316.56 1
1.0%
73552.8 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:40:49.795410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.74
Min length8

Characters and Unicode

Total characters1074
Distinct characters109
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기 평택 진위 신
2nd row경기 평택 진위 신
3rd row경기 파주 조리 장곡
4th row경기 파주 조리 장곡
5th row경기 파주 문산 당동
ValueCountFrequency (%)
경기 100
25.6%
평택 14
 
3.6%
용인 12
 
3.1%
광주 10
 
2.6%
포천 8
 
2.1%
가평 8
 
2.1%
여주 8
 
2.1%
안성 6
 
1.5%
양평 6
 
1.5%
화성 6
 
1.5%
Other values (94) 212
54.4%
2023-12-10T22:40:50.370651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290
27.0%
기 102
 
9.5%
경 100
 
9.3%
평 36
 
3.4%
주 32
 
3.0%
성 24
 
2.2%
양 20
 
1.9%
천 18
 
1.7%
인 16
 
1.5%
용 16
 
1.5%
Other values (99) 420
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 784
73.0%
Space Separator 290
 
27.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 36
 
4.6%
주 32
 
4.1%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
인 16
 
2.0%
용 16
 
2.0%
포 16
 
2.0%
Other values (98) 404
51.5%
Space Separator
ValueCountFrequency (%)
290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 784
73.0%
Common 290
 
27.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 36
 
4.6%
주 32
 
4.1%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
인 16
 
2.0%
용 16
 
2.0%
포 16
 
2.0%
Other values (98) 404
51.5%
Common
ValueCountFrequency (%)
290
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 784
73.0%
ASCII 290
 
27.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290
100.0%
Hangul
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 36
 
4.6%
주 32
 
4.1%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
인 16
 
2.0%
용 16
 
2.0%
포 16
 
2.0%
Other values (98) 404
51.5%

Interactions

2023-12-10T22:40:42.031258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:31.736548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.746599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:33.761867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:34.888216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:36.257656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:38.659517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.764953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:40.903159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:42.143340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:31.819426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.852227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:33.880852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:35.014212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:36.400119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:38.753409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.881016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:41.032863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:42.253337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:31.934652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.946214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:34.015649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:35.152773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:36.533176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:38.895949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.981731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:41.140791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:42.376448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.056448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:33.041663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:34.146331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:35.290228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:36.671125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.024772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:40.117786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:41.273449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:42.493236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.173352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:33.135637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:34.297346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:35.472857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:36.791908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.157122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:40.240370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:41.388084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:42.621157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.275045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:33.255825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:34.467145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:35.614179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:36.929563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.287892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:40.359040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:41.524064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:42.735371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.381135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:33.373450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:34.563545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:35.815081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:37.208684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.396867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:40.486154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:41.660183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:42.861135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.497236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:33.509639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:34.664429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:35.947845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:38.352693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.531719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:40.621584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:41.784124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:42.961843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:32.631298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:33.635388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:34.772338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:36.116398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:38.547437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:39.648891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:40.773501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:41.909757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:40:50.538010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
기본키1.0001.0000.0001.0000.5690.8460.8220.3910.1560.4430.3390.3811.000
지점1.0001.0000.0001.0001.0001.0001.0000.9090.8060.8650.7990.9061.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.1510.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9090.8060.8650.7990.9061.000
연장((km))0.5691.0000.0001.0001.0000.5780.6170.3630.0000.3570.2970.3481.000
좌표위치위도((°))0.8461.0000.0001.0000.5781.0000.8180.3100.1570.4020.2790.3581.000
좌표위치경도((°))0.8221.0000.0001.0000.6170.8181.0000.2810.0000.2380.0000.3641.000
co((g/km))0.3910.9090.0000.9090.3630.3100.2811.0000.9210.9810.9670.9980.909
nox((g/km))0.1560.8060.1510.8060.0000.1570.0000.9211.0000.9530.9530.9270.806
hc((g/km))0.4430.8650.0000.8650.3570.4020.2380.9810.9531.0000.9770.9810.865
pm((g/km))0.3390.7990.0000.7990.2970.2790.0000.9670.9530.9771.0000.9700.799
co2((g/km))0.3810.9060.0000.9060.3480.3580.3640.9980.9270.9810.9701.0000.906
주소1.0001.0000.0001.0001.0001.0001.0000.9090.8060.8650.7990.9061.000
2023-12-10T22:40:50.762758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))방향
기본키1.000-0.236-0.0770.0320.0120.0310.0260.0570.0260.000
연장((km))-0.2361.0000.1910.020-0.026-0.036-0.037-0.024-0.0270.000
좌표위치위도((°))-0.0770.1911.0000.099-0.255-0.330-0.304-0.334-0.2590.000
좌표위치경도((°))0.0320.0200.0991.000-0.122-0.159-0.132-0.146-0.1360.000
co((g/km))0.012-0.026-0.255-0.1221.0000.9700.9870.9370.9970.000
nox((g/km))0.031-0.036-0.330-0.1590.9701.0000.9920.9860.9700.106
hc((g/km))0.026-0.037-0.304-0.1320.9870.9921.0000.9720.9830.000
pm((g/km))0.057-0.024-0.334-0.1460.9370.9860.9721.0000.9380.000
co2((g/km))0.026-0.027-0.259-0.1360.9970.9700.9830.9381.0000.000
방향0.0000.0000.0000.0000.0000.1060.0000.0000.0001.000

Missing values

2023-12-10T22:40:43.123290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:40:43.454481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
01건기연[0134-0]1송탄-오산6.220210501037.09529127.06364288.85223.4131.310.5567484.47경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210501037.09529127.06364289.67252.4432.0512.3473031.59경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210501037.73328126.83253139.3129.7916.89.4335115.87경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210501037.73328126.8325362.958.367.513.9516013.36경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210501037.87708126.7794624.5819.742.280.846887.6경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210501037.87708126.7794640.2126.783.961.19547.2경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210501037.19066127.5599458.1640.226.332.9213787.9경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210501037.19066127.5599462.9440.276.592.414985.32경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210501037.31793127.42763172.53115.7418.346.1840723.75경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210501037.31793127.42763167.0114.3816.716.1343256.93경기 이천 신둔 수하
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4508-0]1포곡-광주3.120210501037.34342127.25053110.33115.5315.67.5926410.42경기 용인 모현 왕산
9192건기연[4508-0]2포곡-광주3.120210501037.34342127.25053151.18161.0820.699.9234355.25경기 용인 모현 왕산
9293건기연[4509-0]1광주-팔당19.020210501037.4815127.2810134.2925.213.741.528738.64경기 광주 남종 삼성
9394건기연[4509-0]2광주-팔당19.020210501037.4815127.2810150.1843.215.953.413351.76경기 광주 남종 삼성
9495건기연[4512-1]1화도-청평8.320210501037.68735127.37997126.74109.9213.546.3234470.56경기 가평 청평 대성
9596건기연[4512-1]2화도-청평8.320210501037.68735127.37997156.09116.3917.176.1336570.48경기 가평 청평 대성
9697건기연[4606-2]1청평-가평2.920210501037.76396127.44335117.1177.6111.93.4627933.97경기 가평 청평 상천
9798건기연[4606-2]2청평-가평2.920210501037.76396127.4433591.2266.648.73.5925481.59경기 가평 청평 상천
9899건기연[4707-0]1내각-부평6.820210501037.70419127.17103343.3290.1641.6715.4180403.64경기 남양주 진접 내각
99100건기연[4707-0]2내각-부평6.820210501037.70419127.17103232.99201.1228.0911.5255171.58경기 남양주 진접 내각